Modern recommendation applications typically provide one of three basic services: providing candidate recommended content for a user, providing candidate related content to an item or group of items, and defining a distance metric between users and content. Many such applications rely on techniques such as collaborative filtering, which may be reasonably successful with the first two services but fails to adequately provide the third service for any but the most mainstream content due to its inability to handle data sparsity, and often becomes intractably slow as the size of the content domain increases. Techniques like singular value decomposition improve on collaborative filtering's failures with sparse data, but also abstract and compress the data in a form that is no longer understandable by human administrators and thus cannot be readily edited or analyzed. Because details can make or break a recommendations platform, singular value decomposition techniques often fall short.